fix style

Author: echarlaix

optimum/intel/openvino/modeling_base.py

@@ -150,12 +150,10 @@ def _save_pretrained(self, save_directory: Union[str, Path]):
 
         self._save_openvino_config(save_directory)
 
-
     def _save_openvino_config(self, save_directory: Union[str, Path]):
         if self._openvino_config is not None:
             self._openvino_config.save_pretrained(save_directory)
 
-
     @classmethod
     def _from_pretrained(
         cls,
@@ -216,12 +214,21 @@ def _from_pretrained(
             local_files_only=local_files_only,
         )
 
-        quantization_config = self._prepare_quantization_config(quantization_config, load_in_8bit)
+        quantization_config = cls._prepare_quantization_config(quantization_config, load_in_8bit)
 
         model = cls.load_model(model_cache_path, quantization_config=quantization_config)
-        return cls(model, config=config, model_save_dir=model_cache_path.parent, quantization_config=quantization_config, **kwargs)
+        return cls(
+            model,
+            config=config,
+            model_save_dir=model_cache_path.parent,
+            quantization_config=quantization_config,
+            **kwargs,
+        )
 
-    def _prepare_quantization_config(quantization_config : Optional[Union[OVWeightQuantizationConfig, Dict]] = None, load_in_8bit:bool= False):
+    @staticmethod
+    def _prepare_quantization_config(
+        quantization_config: Optional[Union[OVWeightQuantizationConfig, Dict]] = None, load_in_8bit: bool = False
+    ):
         # Give default quantization config if not provided and load_in_8bit=True
         if not quantization_config and load_in_8bit:
             quantization_config = OVWeightQuantizationConfig(bits=8)

optimum/intel/openvino/modeling_base_seq2seq.py

@@ -164,7 +164,7 @@ def _from_pretrained(
         decoder_with_past_file_name = decoder_with_past_file_name or default_decoder_with_past_file_name
         decoder_with_past = None
 
-        quantization_config = self._prepare_quantization_config(quantization_config, load_in_8bit)
+        quantization_config = cls._prepare_quantization_config(quantization_config, load_in_8bit)
 
         # Load model from a local directory
         if os.path.isdir(model_id):

optimum/intel/openvino/modeling_decoder.py

@@ -581,9 +581,9 @@ def _from_pretrained(
         )
 
         if isinstance(quantization_config, dict) and quantization_config == {"bits": 4}:
-            quantization_config = _DEFAULT_4BIT_CONFIGS.get(config.name_or_path, quantization_config) 
+            quantization_config = _DEFAULT_4BIT_CONFIGS.get(config.name_or_path, quantization_config)
 
-        quantization_config = self._prepare_quantization_config(quantization_config, load_in_8bit)
+        quantization_config = cls._prepare_quantization_config(quantization_config, load_in_8bit)
 
         load_in_4bit = quantization_config.bits == 4 if quantization_config else False
         model = cls.load_model(model_cache_path, quantization_config=None if load_in_4bit else quantization_config)
@@ -602,7 +602,12 @@ def _from_pretrained(
 
         enable_compilation = kwargs.pop("compile", True) and not load_in_4bit
         causal_model = init_cls(
-            model=model, config=config, model_save_dir=model_cache_path.parent, compile=enable_compilation, quantization_config=quantization_config, **kwargs
+            model=model,
+            config=config,
+            model_save_dir=model_cache_path.parent,
+            compile=enable_compilation,
+            quantization_config=quantization_config,
+            **kwargs,
         )
 
         if load_in_4bit:

optimum/intel/openvino/modeling_diffusion.py

@@ -145,7 +145,6 @@ def __init__(
         if quantization_config:
             self._openvino_config = OVConfig(quantization_config=quantization_config)
 
-
     def _save_pretrained(self, save_directory: Union[str, Path]):
         """
         Saves the model to the OpenVINO IR format so that it can be re-loaded using the
@@ -265,7 +264,7 @@ def _from_pretrained(
                 else:
                     kwargs[name] = load_method(new_model_save_dir)
 
-        quantization_config = self._prepare_quantization_config(quantization_config, load_in_8bit)
+        quantization_config = cls._prepare_quantization_config(quantization_config, load_in_8bit)
         unet = cls.load_model(
             new_model_save_dir / DIFFUSION_MODEL_UNET_SUBFOLDER / unet_file_name, quantization_config
         )
@@ -283,7 +282,14 @@ def _from_pretrained(
         if model_save_dir is None:
             model_save_dir = new_model_save_dir
 
-        return cls(unet=unet, config=config, model_save_dir=model_save_dir, quantization_config=quantization_config, **components, **kwargs)
+        return cls(
+            unet=unet,
+            config=config,
+            model_save_dir=model_save_dir,
+            quantization_config=quantization_config,
+            **components,
+            **kwargs,
+        )
 
     @classmethod
     def _from_transformers(

optimum/intel/openvino/quantization.py

@@ -44,7 +44,7 @@
 from ..utils.constant import _TASK_ALIASES
 from ..utils.import_utils import DATASETS_IMPORT_ERROR, is_datasets_available
 from ..utils.modeling_utils import get_model_device
-from .configuration import OVConfig, OVWeightQuantizationConfig, DEFAULT_QUANTIZATION_CONFIG
+from .configuration import DEFAULT_QUANTIZATION_CONFIG, OVConfig, OVWeightQuantizationConfig
 from .modeling_base import OVBaseModel
 from .utils import (
     MAX_ONNX_OPSET,

optimum/intel/openvino/trainer.py

@@ -89,7 +89,7 @@
 
 from ..utils.constant import _TASK_ALIASES
 from ..utils.import_utils import is_transformers_version
-from .configuration import OVConfig, DEFAULT_QUANTIZATION_CONFIG
+from .configuration import DEFAULT_QUANTIZATION_CONFIG, OVConfig
 from .quantization import OVDataLoader
 from .training_args import OVTrainingArguments
 from .utils import (